R for Data Science. Import, Tidy, Transform, Visualize, and Model Data

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Spis treści książki

• Preface
  • What You Will Learn
  • How This Book Is Organized
  • What You Wont Learn
    • Big Data
    • Python, Julia, and Friends
    • Nonrectangular Data
    • Hypothesis Confirmation
  • Prerequisites
    • R
    • RStudio
    • The Tidyverse
    • Other Packages
  • Running R Code
  • Getting Help and Learning More
  • Acknowledgments
  • Online Version
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Safari
  • How to Contact Us
• I. Explore
• 1. Data Visualization with ggplot2
  • Introduction
    • Prerequisites
  • First Steps
    • The mpg Data Frame
    • Creating a ggplot
    • A Graphing Template
    • Exercises
  • Aesthetic Mappings
    • Exercises
  • Common Problems
  • Facets
    • Exercises
  • Geometric Objects
    • Exercises
  • Statistical Transformations
    • Exercises
  • Position Adjustments
    • Exercises
  • Coordinate Systems
    • Exercises
  • The Layered Grammar of Graphics
• 2. Workflow: Basics
  • Coding Basics
  • Whats in a Name?
  • Calling Functions
    • Exercises
• 3. Data Transformation with dplyr
  • Introduction
    • Prerequisites
    • nycflights13
    • dplyr Basics
  • Filter Rows with filter()
    • Comparisons
    • Logical Operators
    • Missing Values
    • Exercises
  • Arrange Rows with arrange()
    • Exercises
  • Select Columns with select()
    • Exercises
  • Add New Variables with mutate()
    • Useful Creation Functions
    • Exercises
  • Grouped Summaries with summarize()
    • Combining Multiple Operations with the Pipe
    • Missing Values
    • Counts
    • Useful Summary Functions
    • Grouping by Multiple Variables
    • Ungrouping
    • Exercises
  • Grouped Mutates (and Filters)
    • Exercises
• 4. Workflow: Scripts
  • Running Code
  • RStudio Diagnostics
    • Exercises
• 5. Exploratory Data Analysis
  • Introduction
    • Prerequisites
  • Questions
  • Variation
    • Visualizing Distributions
    • Typical Values
    • Unusual Values
    • Exercises
  • Missing Values
    • Exercises
  • Covariation
    • A Categorical and Continuous Variable
    • Exercises
    • Two Categorical Variables
    • Exercises
    • Two Continuous Variables
    • Exercises
  • Patterns and Models
  • ggplot2 Calls
  • Learning More
• 6. Workflow: Projects
  • What Is Real?
  • Where Does Your Analysis Live?
  • Paths and Directories
  • RStudio Projects
  • Summary
• II. Wrangle
• 7. Tibbles with tibble
  • Introduction
    • Prerequisites
  • Creating Tibbles
  • Tibbles Versus data.frame
    • Printing
    • Subsetting
  • Interacting with Older Code
    • Exercises
• 8. Data Import with readr
  • Introduction
    • Prerequisites
  • Getting Started
    • Compared to Base R
    • Exercises
  • Parsing a Vector
    • Numbers
    • Strings
    • Factors
    • Dates, Date-Times, and Times
    • Exercises
  • Parsing a File
    • Strategy
    • Problems
    • Other Strategies
  • Writing to a File
  • Other Types of Data
• 9. Tidy Data with tidyr
  • Introduction
    • Prerequisites
  • Tidy Data
    • Exercises
  • Spreading and Gathering
    • Gathering
    • Spreading
    • Exercises
  • Separating and Pull
    • Separate
    • Unite
    • Exercises
  • Missing Values
    • Exercises
  • Case Study
    • Exercises
  • Nontidy Data
• 10. Relational Data with dplyr
  • Introduction
    • Prerequisites
  • nycflights13
    • Exercises
  • Keys
    • Exercises
  • Mutating Joins
    • Understanding Joins
    • Inner Join
    • Outer Joins
    • Duplicate Keys
    • Defining the Key Columns
    • Exercises
    • Other Implementations
  • Filtering Joins
    • Exercises
  • Join Problems
  • Set Operations
• 11. Strings with stringr
  • Introduction
    • Prerequisites
  • String Basics
    • String Length
    • Combining Strings
    • Subsetting Strings
    • Locales
    • Exercises
  • Matching Patterns with Regular Expressions
    • Basic Matches
    • Exercises
    • Anchors
    • Exercises
    • Character Classes and Alternatives
    • Exercises
    • Repetition
    • Exercises
    • Grouping and Backreferences
    • Exercises
  • Tools
    • Detect Matches
    • Exercises
    • Extract Matches
    • Exercises
    • Grouped Matches
    • Exercises
    • Replacing Matches
    • Exercises
    • Splitting
    • Exercises
    • Find Matches
  • Other Types of Pattern
    • Exercises
  • Other Uses of Regular Expressions
  • stringi
    • Exercises
• 12. Factors with forcats
  • Introduction
    • Prerequisites
  • Creating Factors
  • General Social Survey
    • Exercises
  • Modifying Factor Order
    • Exercises
  • Modifying Factor Levels
    • Exercises
• 13. Dates and Times with lubridate
  • Introduction
    • Prerequisites
  • Creating Date/Times
    • From Strings
    • From Individual Components
    • From Other Types
    • Exercises
  • Date-Time Components
    • Getting Components
    • Rounding
    • Setting Components
    • Exercises
  • Time Spans
    • Durations
    • Periods
    • Intervals
    • Summary
    • Exercises
  • Time Zones
• III. Program
• 14. Pipes with magrittr
  • Introduction
    • Prerequisites
  • Piping Alternatives
    • Intermediate Steps
    • Overwrite the Original
    • Function Composition
    • Use the Pipe
  • When Not to Use the Pipe
  • Other Tools from magrittr
• 15. Functions
  • Introduction
    • Prerequisites
  • When Should You Write a Function?
    • Exercises
  • Functions Are for Humans and Computers
    • Exercises
  • Conditional Execution
    • Conditions
    • Multiple Conditions
    • Code Style
    • Exercises
  • Function Arguments
    • Choosing Names
    • Checking Values
    • Dot-Dot-Dot ()
    • Lazy Evaluation
    • Exercises
  • Return Values
    • Explicit Return Statements
    • Writing Pipeable Functions
  • Environment
• 16. Vectors
  • Introduction
    • Prerequisites
  • Vector Basics
  • Important Types of Atomic Vector
    • Logical
    • Numeric
    • Character
    • Missing Values
    • Exercises
  • Using Atomic Vectors
    • Coercion
    • Test Functions
    • Scalars and Recycling Rules
    • Naming Vectors
    • Subsetting
    • Exercises
  • Recursive Vectors (Lists)
    • Visualizing Lists
    • Subsetting
    • Lists of Condiments
    • Exercises
  • Attributes
  • Augmented Vectors
    • Factors
    • Dates and Date-Times
    • Tibbles
    • Exercises
• 17. Iteration with purrr
  • Introduction
    • Prerequisites
  • For Loops
    • Exercises
  • For Loop Variations
    • Modifying an Existing Object
    • Looping Patterns
    • Unknown Output Length
    • Unknown Sequence Length
    • Exercises
  • For Loops Versus Functionals
    • Exercises
  • The Map Functions
    • Shortcuts
    • Base R
    • Exercises
  • Dealing with Failure
  • Mapping over Multiple Arguments
    • Invoking Different Functions
  • Walk
  • Other Patterns of For Loops
    • Predicate Functions
    • Reduce and Accumulate
    • Exercises
• IV. Model
• 18. Model Basics with modelr
  • Introduction
    • Prerequisites
  • A Simple Model
    • Exercises
  • Visualizing Models
    • Predictions
    • Residuals
    • Exercises
  • Formulas and Model Families
    • Categorical Variables
    • Interactions (Continuous and Categorical)
    • Interactions (Two Continuous)
    • Transformations
    • Exercises
  • Missing Values
  • Other Model Families
• 19. Model Building
  • Introduction
    • Prerequisites
  • Why Are Low-Quality Diamonds More Expensive?
    • Price and Carat
    • A More Complicated Model
    • Exercises
  • What Affects the Number of Daily Flights?
    • Day of Week
    • Seasonal Saturday Effect
    • Computed Variables
    • Time of Year: An Alternative Approach
    • Exercises
  • Learning More About Models
• 20. Many Models with purrr and broom
  • Introduction
    • Prerequisites
  • gapminder
    • Nested Data
    • List-Columns
    • Unnesting
    • Model Quality
    • Exercises
  • List-Columns
  • Creating List-Columns
    • With Nesting
    • From Vectorized Functions
    • From Multivalued Summaries
    • From a Named List
    • Exercises
  • Simplifying List-Columns
    • List to Vector
    • Unnesting
    • Exercises
  • Making Tidy Data with broom
• V. Communicate
• 21. R Markdown
  • Introduction
    • Prerequisites
  • R Markdown Basics
    • Exercises
  • Text Formatting with Markdown
    • Exercises
  • Code Chunks
    • Chunk Name
    • Chunk Options
    • Table
    • Caching
    • Global Options
    • Inline Code
    • Exercises
  • Troubleshooting
  • YAML Header
    • Parameters
    • Bibliographies and Citations
  • Learning More
• 22. Graphics for Communication with ggplot2
  • Introduction
    • Prerequisites
  • Label
    • Exercises
  • Annotations
    • Exercises
  • Scales
    • Axis Ticks and Legend Keys
    • Legend Layout
    • Replacing a Scale
    • Exercises
  • Zooming
  • Themes
  • Saving Your Plots
    • Figure Sizing
    • Other Important Options
  • Learning More
• 23. R Markdown Formats
  • Introduction
  • Output Options
  • Documents
  • Notebooks
  • Presentations
  • Dashboards
  • Interactivity
    • htmlwidgets
    • Shiny
  • Websites
  • Other Formats
  • Learning More
• 24. R Markdown Workflow
• Index

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